Réf ABG-139632 Sujet de Thèse
21/06/2026 Financement public/privé
CESI
Lieu de travail
Strasbourg - Grand Est - France
Intitulé du sujet
PhD position (IA + Geophysics) : Pipeline leak detection using electromagnetic scanning powered by distributed AI (LEAK-SCAN)
Champs scientifiques
Informatique
Description du sujet
The future PhD student will be registered at ENSAM (École nationale supérieure d'arts et métiers ).
Research Work
Scientific Context
Water leakage in urban underground pipelines can not only bring economic loss due to the wastage, but also can lead to structural damages of roads and buildings, which brings severe problems in the city infrastructure management. Addressing these issues requires efficient detection to minimize the impact on public safety. There are several methods for detecting water leaks, such as pressure/flow sensors or fiber optics, but they require sensors to be installed in advance. To complete the existing techniques, in this study, we will explore the application of ground penetrating radar (GPR) to detect underground water leakage, because GPR is non-destructive and high-efficient way.
Thesis Objective
This thesis aims to detect water leakages without the need for excavation based on radar methods. The physical basis of radar depends on the dielectric contrast between dry and wet soil around the pipes. This contrast appears as an anomaly in the radargram, providing an indication of the potential leak location.
Based on the previous research works, Leak-scan proposes to use 3D GPR data to optimize the detection process powered by AI techniques. Its objectives contain (1) inverse the time-domain data into spatial domain based on full wave inversion (FWI); (2) combine the simulated data with experimental data to build hybrid dataset, (3) use learning-based methods to detect the GPR images with and without leakage.
Concretely, this doctoral project will aim to design a comprehensive GPR detection solution. Indeed, to implement this technique with high precision and obtain a real-time view of the soil condition, certain scientific hurdles remain to be overcome. The challenge lies in identifying water leaks with the consideration of the nature of soils, the multi-layered structure in the urban trench construction, the volume of leaked water, and the pipe dimensions, etc. Meanwhile, the AI modeling of wave propagation in the ground will be applied in the interpretation of data. This process is supported by a federated learning infrastructure that combines data from multiple sources to build a global perspective. The entire framework leverages an edge-cloud infrastructure to ensure real-time responsiveness.
The data used in this thesis will be a hybrid one: the combination of the collected experimental data from the Project Radir and the simulated data from gprMax.
Thesis Work Planning
The provisional work plan is detailed as follows :
— Enrollment in Doctoral School 432 (ED SMI 432 - SCIENCES DES MÉTIERS DE L'INGÉNIEUR), state of the art review, and definition of a research methodology.
— Modeling the behavior of electromagnetic waves in unsaturated soil, paper submission.
— Development of a reliable and efficient PINNs-FWI model for the detection and characterization of water leaks. Paper submission
— Proposal of innovative methods for optimizing AI models and distributed computing, and paper submission
— Evaluation of field strategies for the experiments obtained by Project Radir, article submission
— Dissertation manuscript written, presentation of results, defense.
Expected Scientific/Technical Output
The research results are expected to be published in top-tier international conferences and journals.
The thesis will lead to the development of a complete GPR solution for pipeline leak detection
Nature du financement
Financement public/privé
Précisions sur le financement
Présentation établissement et labo d'accueil
CESI
Laboratory Presentation: CESI LINEACT
CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research ; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions. Its research is organized according to two interdisciplinary scientific teams and several application areas.
— Team 1 "Learning and Innovating" mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity and innovation processes.
— Team 2 "Engineering and Digital Tools" mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human-system interactions in particular through digital twins coupled with virtual or augmented environments.
These two teams develop and cross their research in application areas such as
— Industry 5.0,
— Construction 4.0 and Sustainable City,
— Digital Services.
Areas supported by research platforms, mainly those in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.
Website of CESI LINEACT: https://lineact.cesi.fr/
Laboratory Presentation: CEREMA et ENDSUM
Cerema (which stands for Centre for Studies and Expertise on Risks, the Environment, Mobility and Urban Planning) is the major French public agency for developing public expertise in the fields of urban planning, regional cohesion and ecological and energy transition for resilient and climate-neutral cities and regions. Cerema was created in 2014 by merging eleven public expertise organizations, each with decades of experience in the fields of bridges, roads and ports infrastructure, water, geotechnics, risk, land use and urban development.
ENDSUM (Non-Destructive Evaluation of Structures and Materials) is one of the research teams in Cerema. It seeks to address several key economic, societal, and strategic challenges in the fields of civil engineering and Earth sciences in the context of global climate change. Its mission is to generate scientific knowledge and develop practical tools:
(1) to support Infrastructure Asset Management (IAM) by ensuring that infrastructure systems are maintained at appropriate levels of serviceability and safety;
(2) to contribute to the formulation of public policies aimed at the prevention and management of natural and human-induced hazards.
Website of ENDSUM: https://www.cerema.fr/fr/innovation-recherche/recherche/equipes/endsum-evaluation-non-destructive-structures-materiaux
Profil du candidat
Pré-requis du poste
Required Competences
Scientific and technical skills in one or more of the following areas:
— Strong mathematical skills, particularly in convex and non-convex optimization,
matrix theory, and probability.
— Strong background in signal processing, such as detection and estimation theory.
— Background with electromagnetic wave and Maxwell equations is appreciated
— Experience with gprMax is appreciated
— Proficiency in key AI techniques, experience with PyTorch or TensorFlow would be
appreciated.
— Strong programming and networking skills.
— Interest in high-performance computing.
Soft skills:
— Good level of written and spoken English.
— Ability to work independently, with initiative and curiosity.
— Ability to work in a team and maintain good interpersonal relations.
— Attention to detail and rigor.
